Most computational models of musical understanding have focused on procedural aspects of analysis, suggesting techniques for parsing, comparing, and transforming various representations of a piece, or adapting discovery procedures of artificially intelligent (AI) inference systems, which plan and follow agendas and goals. Much contemporary AI research, however, also focuses on declarative aspects of knowledge, attempting to define data representations and relations that are commensurate with human cognition. Naturally, musical analysis has both procedural and declarative aspects: the declarative determines what the form of the analysis is, and the procedural determines how the analysis is obtained. However, a predominantly procedural analysis risks sacrificing the form of musical understanding to obtain efficiency or compatibility with a particular computer language. In this article I argue that, for a significant body of twentieth-century music, a declarative system models the structure of analytical understanding better than do existing procedural programs, and I present a functioning declarative system that infers complex musical structures from the elementary musical relations that it identifies.

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